Overview

Dataset statistics

Number of variables34
Number of observations325
Missing cells196
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.5 KiB
Average record size in memory272.4 B

Variable types

Categorical7
Numeric27

Alerts

Season has constant value "2021" Constant
Player has a high cardinality: 325 distinct values High cardinality
Pass % has a high cardinality: 197 distinct values High cardinality
xPass % has a high cardinality: 181 distinct values High cardinality
Touch % has a high cardinality: 116 distinct values High cardinality
Minutes is highly correlated with Shots and 9 other fieldsHigh correlation
Shots is highly correlated with Minutes and 11 other fieldsHigh correlation
SoT is highly correlated with Minutes and 10 other fieldsHigh correlation
G is highly correlated with Shots and 9 other fieldsHigh correlation
xG is highly correlated with Minutes and 10 other fieldsHigh correlation
xPlace is highly correlated with G-xGHigh correlation
G-xG is highly correlated with xPlaceHigh correlation
KeyP is highly correlated with Minutes and 11 other fieldsHigh correlation
A is highly correlated with Minutes and 9 other fieldsHigh correlation
xA is highly correlated with Minutes and 11 other fieldsHigh correlation
xG+xA is highly correlated with Minutes and 11 other fieldsHigh correlation
PA is highly correlated with Shots and 8 other fieldsHigh correlation
xPA is highly correlated with Minutes and 10 other fieldsHigh correlation
Passes is highly correlated with Minutes and 5 other fieldsHigh correlation
Score is highly correlated with Per100High correlation
Per100 is highly correlated with ScoreHigh correlation
Distance is highly correlated with VerticalHigh correlation
Vertical is highly correlated with DistanceHigh correlation
Games is highly correlated with Minutes and 11 other fieldsHigh correlation
Receiving is highly correlated with ShootingHigh correlation
Shooting is highly correlated with ReceivingHigh correlation
Minutes is highly correlated with Shots and 5 other fieldsHigh correlation
Shots is highly correlated with Minutes and 11 other fieldsHigh correlation
SoT is highly correlated with Shots and 10 other fieldsHigh correlation
G is highly correlated with Shots and 7 other fieldsHigh correlation
xG is highly correlated with Shots and 7 other fieldsHigh correlation
xPlace is highly correlated with G-xGHigh correlation
G-xG is highly correlated with G and 1 other fieldsHigh correlation
KeyP is highly correlated with Minutes and 7 other fieldsHigh correlation
A is highly correlated with Shots and 5 other fieldsHigh correlation
xA is highly correlated with Minutes and 6 other fieldsHigh correlation
A-xA is highly correlated with AHigh correlation
xG+xA is highly correlated with Minutes and 11 other fieldsHigh correlation
PA is highly correlated with Shots and 6 other fieldsHigh correlation
xPA is highly correlated with Shots and 7 other fieldsHigh correlation
Passes is highly correlated with Minutes and 2 other fieldsHigh correlation
Distance is highly correlated with VerticalHigh correlation
Vertical is highly correlated with DistanceHigh correlation
Games is highly correlated with Minutes and 6 other fieldsHigh correlation
Dribbling is highly correlated with Goals AddedHigh correlation
Receiving is highly correlated with xG and 2 other fieldsHigh correlation
Shooting is highly correlated with Shots and 7 other fieldsHigh correlation
Goals Added is highly correlated with DribblingHigh correlation
Minutes is highly correlated with KeyP and 2 other fieldsHigh correlation
Shots is highly correlated with SoT and 9 other fieldsHigh correlation
SoT is highly correlated with Shots and 8 other fieldsHigh correlation
G is highly correlated with Shots and 5 other fieldsHigh correlation
xG is highly correlated with Shots and 7 other fieldsHigh correlation
xPlace is highly correlated with G-xGHigh correlation
G-xG is highly correlated with xPlaceHigh correlation
KeyP is highly correlated with Minutes and 8 other fieldsHigh correlation
A is highly correlated with Shots and 4 other fieldsHigh correlation
xA is highly correlated with Shots and 7 other fieldsHigh correlation
xG+xA is highly correlated with Shots and 9 other fieldsHigh correlation
PA is highly correlated with Shots and 5 other fieldsHigh correlation
xPA is highly correlated with Shots and 7 other fieldsHigh correlation
Passes is highly correlated with Minutes and 1 other fieldsHigh correlation
Score is highly correlated with Per100High correlation
Per100 is highly correlated with ScoreHigh correlation
Distance is highly correlated with VerticalHigh correlation
Vertical is highly correlated with DistanceHigh correlation
Games is highly correlated with Minutes and 5 other fieldsHigh correlation
Position is highly correlated with SeasonHigh correlation
Team is highly correlated with SeasonHigh correlation
Season is highly correlated with Position and 1 other fieldsHigh correlation
Position is highly correlated with Distance and 1 other fieldsHigh correlation
Minutes is highly correlated with Shots and 10 other fieldsHigh correlation
Shots is highly correlated with Minutes and 20 other fieldsHigh correlation
SoT is highly correlated with Minutes and 18 other fieldsHigh correlation
G is highly correlated with Shots and 14 other fieldsHigh correlation
xG is highly correlated with Shots and 14 other fieldsHigh correlation
xPlace is highly correlated with Shots and 15 other fieldsHigh correlation
G-xG is highly correlated with Shots and 14 other fieldsHigh correlation
KeyP is highly correlated with Minutes and 16 other fieldsHigh correlation
A is highly correlated with Minutes and 12 other fieldsHigh correlation
xA is highly correlated with Minutes and 19 other fieldsHigh correlation
A-xA is highly correlated with Shots and 9 other fieldsHigh correlation
xG+xA is highly correlated with Minutes and 19 other fieldsHigh correlation
PA is highly correlated with Shots and 14 other fieldsHigh correlation
xPA is highly correlated with Shots and 14 other fieldsHigh correlation
Passes is highly correlated with Minutes and 9 other fieldsHigh correlation
Score is highly correlated with Shots and 11 other fieldsHigh correlation
Per100 is highly correlated with ScoreHigh correlation
Distance is highly correlated with Position and 1 other fieldsHigh correlation
Vertical is highly correlated with Position and 1 other fieldsHigh correlation
Games is highly correlated with Minutes and 6 other fieldsHigh correlation
Dribbling is highly correlated with Shots and 13 other fieldsHigh correlation
Fouling is highly correlated with Shots and 7 other fieldsHigh correlation
Interrupting is highly correlated with Passes and 1 other fieldsHigh correlation
Passing is highly correlated with KeyP and 7 other fieldsHigh correlation
Receiving is highly correlated with Minutes and 16 other fieldsHigh correlation
Shooting is highly correlated with Minutes and 18 other fieldsHigh correlation
Goals Added is highly correlated with Minutes and 17 other fieldsHigh correlation
Dribbling has 28 (8.6%) missing values Missing
Fouling has 28 (8.6%) missing values Missing
Interrupting has 28 (8.6%) missing values Missing
Passing has 28 (8.6%) missing values Missing
Receiving has 28 (8.6%) missing values Missing
Shooting has 28 (8.6%) missing values Missing
Goals Added has 28 (8.6%) missing values Missing
Player is uniformly distributed Uniform
xPass % is uniformly distributed Uniform
Player has unique values Unique
Shots has 62 (19.1%) zeros Zeros
SoT has 108 (33.2%) zeros Zeros
G has 190 (58.5%) zeros Zeros
xG has 62 (19.1%) zeros Zeros
xPlace has 62 (19.1%) zeros Zeros
G-xG has 62 (19.1%) zeros Zeros
KeyP has 78 (24.0%) zeros Zeros
A has 190 (58.5%) zeros Zeros
xA has 78 (24.0%) zeros Zeros
A-xA has 78 (24.0%) zeros Zeros
xG+xA has 48 (14.8%) zeros Zeros
PA has 192 (59.1%) zeros Zeros
xPA has 69 (21.2%) zeros Zeros
Passes has 5 (1.5%) zeros Zeros
Score has 5 (1.5%) zeros Zeros
Per100 has 5 (1.5%) zeros Zeros
Distance has 8 (2.5%) zeros Zeros
Vertical has 8 (2.5%) zeros Zeros
Dribbling has 17 (5.2%) zeros Zeros
Fouling has 45 (13.8%) zeros Zeros
Interrupting has 18 (5.5%) zeros Zeros
Passing has 19 (5.8%) zeros Zeros
Receiving has 12 (3.7%) zeros Zeros
Shooting has 19 (5.8%) zeros Zeros
Goals Added has 5 (1.5%) zeros Zeros

Reproduction

Analysis started2022-05-03 18:26:49.837083
Analysis finished2022-05-03 18:28:17.198817
Duration1 minute and 27.36 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Player
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct325
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Erickson Gallardo
 
1
Roman Torres
 
1
Romeo Beckham
 
1
Kevin Silva
 
1
Brandon Fricke
 
1
Other values (320)
320 

Length

Max length23
Median length20
Mean length13.38153846
Min length4

Characters and Unicode

Total characters4349
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique325 ?
Unique (%)100.0%

Sample

1st rowAaron Messer
2nd rowAaron Molloy
3rd rowAaron Walker
4th rowAbdi Mohamed
5th rowAbdul Illal Osumanu

Common Values

ValueCountFrequency (%)
Erickson Gallardo1
 
0.3%
Roman Torres1
 
0.3%
Romeo Beckham1
 
0.3%
Kevin Silva1
 
0.3%
Brandon Fricke1
 
0.3%
Adrian Billhardt1
 
0.3%
Richard Sánchez1
 
0.3%
Bernard Kamungo1
 
0.3%
Alex Knox1
 
0.3%
Julian Altobelli1
 
0.3%
Other values (315)315
96.9%

Length

2022-05-03T11:28:17.280245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
joshua5
 
0.8%
christian5
 
0.8%
alex4
 
0.6%
luis4
 
0.6%
kevin4
 
0.6%
michael4
 
0.6%
jonathan4
 
0.6%
jake4
 
0.6%
noah4
 
0.6%
ethan3
 
0.5%
Other values (544)613
93.7%

Most occurring characters

ValueCountFrequency (%)
a448
 
10.3%
e358
 
8.2%
329
 
7.6%
n302
 
6.9%
i277
 
6.4%
o271
 
6.2%
r267
 
6.1%
l238
 
5.5%
s174
 
4.0%
t125
 
2.9%
Other values (53)1560
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3330
76.6%
Uppercase Letter674
 
15.5%
Space Separator329
 
7.6%
Dash Punctuation11
 
0.3%
Other Punctuation5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a448
13.5%
e358
10.8%
n302
 
9.1%
i277
 
8.3%
o271
 
8.1%
r267
 
8.0%
l238
 
7.1%
s174
 
5.2%
t125
 
3.8%
u108
 
3.2%
Other values (23)762
22.9%
Uppercase Letter
ValueCountFrequency (%)
M71
 
10.5%
C62
 
9.2%
J57
 
8.5%
A48
 
7.1%
R43
 
6.4%
D40
 
5.9%
S37
 
5.5%
B36
 
5.3%
P33
 
4.9%
N28
 
4.2%
Other values (16)219
32.5%
Other Punctuation
ValueCountFrequency (%)
'4
80.0%
.1
 
20.0%
Space Separator
ValueCountFrequency (%)
329
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4004
92.1%
Common345
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a448
 
11.2%
e358
 
8.9%
n302
 
7.5%
i277
 
6.9%
o271
 
6.8%
r267
 
6.7%
l238
 
5.9%
s174
 
4.3%
t125
 
3.1%
u108
 
2.7%
Other values (49)1436
35.9%
Common
ValueCountFrequency (%)
329
95.4%
-11
 
3.2%
'4
 
1.2%
.1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4330
99.6%
None19
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a448
 
10.3%
e358
 
8.3%
329
 
7.6%
n302
 
7.0%
i277
 
6.4%
o271
 
6.3%
r267
 
6.2%
l238
 
5.5%
s174
 
4.0%
t125
 
2.9%
Other values (45)1541
35.6%
None
ValueCountFrequency (%)
á5
26.3%
é4
21.1%
í4
21.1%
ú2
 
10.5%
Á1
 
5.3%
ã1
 
5.3%
ä1
 
5.3%
ó1
 
5.3%

Team
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
TOR
33 
NTX
32 
NC
31 
NER
30 
FTL
29 
Other values (8)
170 

Length

Max length8
Median length3
Mean length2.92
Min length2

Characters and Unicode

Total characters949
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowNC
2nd rowMAD
3rd rowGVL
4th rowGVL
5th rowOMA

Common Values

ValueCountFrequency (%)
TOR33
10.2%
NTX32
9.8%
NC31
9.5%
NER30
9.2%
FTL29
8.9%
RIC26
8.0%
TRM25
7.7%
TUC25
7.7%
OMA24
7.4%
GVL23
7.1%
Other values (3)47
14.5%

Length

2022-05-03T11:28:17.418078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tor33
10.1%
ntx32
9.8%
nc31
9.5%
ner31
9.5%
ftl29
8.9%
ric26
8.0%
trm25
7.7%
tuc25
7.7%
oma24
7.4%
mad24
7.4%
Other values (2)46
14.1%

Most occurring characters

ValueCountFrequency (%)
T144
15.2%
R115
12.1%
C105
11.1%
N94
9.9%
M73
7.7%
A71
7.5%
O57
 
6.0%
L52
 
5.5%
X32
 
3.4%
E31
 
3.3%
Other values (9)175
18.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter947
99.8%
Other Punctuation1
 
0.1%
Space Separator1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T144
15.2%
R115
12.1%
C105
11.1%
N94
9.9%
M73
7.7%
A71
7.5%
O57
 
6.0%
L52
 
5.5%
X32
 
3.4%
E31
 
3.3%
Other values (7)173
18.3%
Other Punctuation
ValueCountFrequency (%)
,1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin947
99.8%
Common2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
T144
15.2%
R115
12.1%
C105
11.1%
N94
9.9%
M73
7.7%
A71
7.5%
O57
 
6.0%
L52
 
5.5%
X32
 
3.4%
E31
 
3.3%
Other values (7)173
18.3%
Common
ValueCountFrequency (%)
,1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T144
15.2%
R115
12.1%
C105
11.1%
N94
9.9%
M73
7.7%
A71
7.5%
O57
 
6.0%
L52
 
5.5%
X32
 
3.4%
E31
 
3.3%
Other values (9)175
18.4%

Season
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2021
325 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1300
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021325
100.0%

Length

2022-05-03T11:28:17.535226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-03T11:28:17.642367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2021325
100.0%

Most occurring characters

ValueCountFrequency (%)
2650
50.0%
0325
25.0%
1325
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2650
50.0%
0325
25.0%
1325
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common1300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2650
50.0%
0325
25.0%
1325
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2650
50.0%
0325
25.0%
1325
25.0%

Position
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
W
61 
CB
59 
CM
59 
ST
51 
FB
50 
Other values (2)
45 

Length

Max length2
Median length2
Mean length1.812307692
Min length1

Characters and Unicode

Total characters589
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCB
2nd rowDM
3rd rowCM
4th rowFB
5th rowCB

Common Values

ValueCountFrequency (%)
W61
18.8%
CB59
18.2%
CM59
18.2%
ST51
15.7%
FB50
15.4%
GK28
8.6%
DM17
 
5.2%

Length

2022-05-03T11:28:17.723557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-03T11:28:17.846575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
w61
18.8%
cb59
18.2%
cm59
18.2%
st51
15.7%
fb50
15.4%
gk28
8.6%
dm17
 
5.2%

Most occurring characters

ValueCountFrequency (%)
C118
20.0%
B109
18.5%
M76
12.9%
W61
10.4%
S51
8.7%
T51
8.7%
F50
8.5%
G28
 
4.8%
K28
 
4.8%
D17
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter589
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C118
20.0%
B109
18.5%
M76
12.9%
W61
10.4%
S51
8.7%
T51
8.7%
F50
8.5%
G28
 
4.8%
K28
 
4.8%
D17
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin589
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C118
20.0%
B109
18.5%
M76
12.9%
W61
10.4%
S51
8.7%
T51
8.7%
F50
8.5%
G28
 
4.8%
K28
 
4.8%
D17
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C118
20.0%
B109
18.5%
M76
12.9%
W61
10.4%
S51
8.7%
T51
8.7%
F50
8.5%
G28
 
4.8%
K28
 
4.8%
D17
 
2.9%

Minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct307
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114.049231
Minimum1
Maximum2757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:17.950526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1337
median1010
Q31777
95-th percentile2570.4
Maximum2757
Range2756
Interquartile range (IQR)1440

Descriptive statistics

Standard deviation831.7457938
Coefficient of variation (CV)0.7465969823
Kurtosis-1.159653397
Mean1114.049231
Median Absolute Deviation (MAD)721
Skewness0.2870714545
Sum362066
Variance691801.0655
MonotonicityNot monotonic
2022-05-03T11:28:18.045614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184
 
1.2%
103
 
0.9%
12
 
0.6%
4162
 
0.6%
9122
 
0.6%
16892
 
0.6%
21832
 
0.6%
18742
 
0.6%
13652
 
0.6%
17022
 
0.6%
Other values (297)302
92.9%
ValueCountFrequency (%)
12
0.6%
41
 
0.3%
52
0.6%
72
0.6%
81
 
0.3%
103
0.9%
111
 
0.3%
121
 
0.3%
161
 
0.3%
171
 
0.3%
ValueCountFrequency (%)
27571
0.3%
27271
0.3%
27021
0.3%
26941
0.3%
26851
0.3%
26841
0.3%
26721
0.3%
26621
0.3%
26611
0.3%
26601
0.3%

Shots
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.57538462
Minimum0
Maximum85
Zeros62
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:18.156286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile45
Maximum85
Range85
Interquartile range (IQR)16

Descriptive statistics

Standard deviation15.42438776
Coefficient of variation (CV)1.226553957
Kurtosis3.950621326
Mean12.57538462
Median Absolute Deviation (MAD)7
Skewness1.905982194
Sum4087
Variance237.9117379
MonotonicityNot monotonic
2022-05-03T11:28:18.261409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062
19.1%
128
 
8.6%
516
 
4.9%
213
 
4.0%
912
 
3.7%
312
 
3.7%
1212
 
3.7%
411
 
3.4%
711
 
3.4%
610
 
3.1%
Other values (44)138
42.5%
ValueCountFrequency (%)
062
19.1%
128
8.6%
213
 
4.0%
312
 
3.7%
411
 
3.4%
516
 
4.9%
610
 
3.1%
711
 
3.4%
810
 
3.1%
912
 
3.7%
ValueCountFrequency (%)
851
0.3%
771
0.3%
692
0.6%
681
0.3%
671
0.3%
641
0.3%
631
0.3%
551
0.3%
521
0.3%
491
0.3%

SoT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.295384615
Minimum0
Maximum40
Zeros108
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:18.393953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile17.8
Maximum40
Range40
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.254293112
Coefficient of variation (CV)1.456049614
Kurtosis6.395723335
Mean4.295384615
Median Absolute Deviation (MAD)2
Skewness2.309443956
Sum1396
Variance39.11618234
MonotonicityNot monotonic
2022-05-03T11:28:18.481239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0108
33.2%
243
 
13.2%
137
 
11.4%
322
 
6.8%
421
 
6.5%
514
 
4.3%
611
 
3.4%
89
 
2.8%
97
 
2.2%
116
 
1.8%
Other values (18)47
14.5%
ValueCountFrequency (%)
0108
33.2%
137
 
11.4%
243
 
13.2%
322
 
6.8%
421
 
6.5%
514
 
4.3%
611
 
3.4%
73
 
0.9%
89
 
2.8%
97
 
2.2%
ValueCountFrequency (%)
401
 
0.3%
341
 
0.3%
311
 
0.3%
262
0.6%
241
 
0.3%
231
 
0.3%
223
0.9%
202
0.6%
194
1.2%
181
 
0.3%

G
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.313846154
Minimum0
Maximum18
Zeros190
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:18.594670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.448221635
Coefficient of variation (CV)1.863400542
Kurtosis11.92152931
Mean1.313846154
Median Absolute Deviation (MAD)0
Skewness3.038098606
Sum427
Variance5.993789174
MonotonicityNot monotonic
2022-05-03T11:28:18.670624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0190
58.5%
148
 
14.8%
229
 
8.9%
315
 
4.6%
415
 
4.6%
510
 
3.1%
65
 
1.5%
83
 
0.9%
92
 
0.6%
102
 
0.6%
Other values (5)6
 
1.8%
ValueCountFrequency (%)
0190
58.5%
148
 
14.8%
229
 
8.9%
315
 
4.6%
415
 
4.6%
510
 
3.1%
65
 
1.5%
71
 
0.3%
83
 
0.9%
92
 
0.6%
ValueCountFrequency (%)
181
 
0.3%
141
 
0.3%
131
 
0.3%
112
 
0.6%
102
 
0.6%
92
 
0.6%
83
 
0.9%
71
 
0.3%
65
1.5%
510
3.1%

xG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct175
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.323907692
Minimum0
Maximum15.62
Zeros62
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:18.772720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.07
median0.56
Q31.61
95-th percentile5.63
Maximum15.62
Range15.62
Interquartile range (IQR)1.54

Descriptive statistics

Standard deviation2.112582482
Coefficient of variation (CV)1.595717356
Kurtosis11.79788813
Mean1.323907692
Median Absolute Deviation (MAD)0.56
Skewness3.04716124
Sum430.27
Variance4.463004745
MonotonicityNot monotonic
2022-05-03T11:28:19.548019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062
 
19.1%
0.025
 
1.5%
0.455
 
1.5%
0.14
 
1.2%
0.434
 
1.2%
0.054
 
1.2%
0.074
 
1.2%
0.374
 
1.2%
0.034
 
1.2%
0.174
 
1.2%
Other values (165)225
69.2%
ValueCountFrequency (%)
062
19.1%
0.011
 
0.3%
0.025
 
1.5%
0.034
 
1.2%
0.041
 
0.3%
0.054
 
1.2%
0.062
 
0.6%
0.074
 
1.2%
0.082
 
0.6%
0.093
 
0.9%
ValueCountFrequency (%)
15.621
0.3%
12.331
0.3%
10.891
0.3%
9.81
0.3%
9.551
0.3%
9.111
0.3%
9.051
0.3%
8.131
0.3%
7.911
0.3%
7.781
0.3%

xPlace
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct147
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03772307692
Minimum-2.67
Maximum2.49
Zeros62
Zeros (%)19.1%
Negative160
Negative (%)49.2%
Memory size2.7 KiB
2022-05-03T11:28:19.673224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.67
5-th percentile-1.006
Q1-0.27
median0
Q30.14
95-th percentile1.032
Maximum2.49
Range5.16
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.6131589805
Coefficient of variation (CV)-16.25421441
Kurtosis4.167486094
Mean-0.03772307692
Median Absolute Deviation (MAD)0.21
Skewness0.257634145
Sum-12.26
Variance0.3759639354
MonotonicityNot monotonic
2022-05-03T11:28:19.762266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062
 
19.1%
-0.018
 
2.5%
-0.067
 
2.2%
-0.095
 
1.5%
-0.225
 
1.5%
-0.135
 
1.5%
-0.035
 
1.5%
-0.45
 
1.5%
-0.175
 
1.5%
0.075
 
1.5%
Other values (137)213
65.5%
ValueCountFrequency (%)
-2.671
0.3%
-2.11
0.3%
-21
0.3%
-1.951
0.3%
-1.771
0.3%
-1.532
0.6%
-1.491
0.3%
-1.282
0.6%
-1.211
0.3%
-1.151
0.3%
ValueCountFrequency (%)
2.491
0.3%
2.411
0.3%
2.331
0.3%
1.981
0.3%
1.751
0.3%
1.591
0.3%
1.561
0.3%
1.491
0.3%
1.361
0.3%
1.321
0.3%

G-xG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct172
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01006153846
Minimum-3.16
Maximum4.31
Zeros62
Zeros (%)19.1%
Negative173
Negative (%)53.2%
Memory size2.7 KiB
2022-05-03T11:28:19.868224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.16
5-th percentile-1.478
Q1-0.4
median-0.03
Q30.18
95-th percentile1.666
Maximum4.31
Range7.47
Interquartile range (IQR)0.58

Descriptive statistics

Standard deviation0.9539181688
Coefficient of variation (CV)-94.80838069
Kurtosis3.413138903
Mean-0.01006153846
Median Absolute Deviation (MAD)0.34
Skewness0.7516593858
Sum-3.27
Variance0.9099598727
MonotonicityNot monotonic
2022-05-03T11:28:19.962120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062
 
19.1%
-0.026
 
1.8%
-0.185
 
1.5%
-0.435
 
1.5%
-0.14
 
1.2%
-0.144
 
1.2%
-0.034
 
1.2%
-0.454
 
1.2%
-0.134
 
1.2%
-0.074
 
1.2%
Other values (162)223
68.6%
ValueCountFrequency (%)
-3.161
0.3%
-2.891
0.3%
-2.621
0.3%
-2.511
0.3%
-2.141
0.3%
-2.121
0.3%
-1.981
0.3%
-1.932
0.6%
-1.911
0.3%
-1.881
0.3%
ValueCountFrequency (%)
4.311
0.3%
3.881
0.3%
3.451
0.3%
3.341
0.3%
3.021
0.3%
2.451
0.3%
2.431
0.3%
2.381
0.3%
2.371
0.3%
2.251
0.3%

KeyP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.775384615
Minimum0
Maximum58
Zeros78
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:20.081148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q314
95-th percentile32.4
Maximum58
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.09562853
Coefficient of variation (CV)1.264403672
Kurtosis3.775592418
Mean8.775384615
Median Absolute Deviation (MAD)4
Skewness1.854174675
Sum2852
Variance123.1129725
MonotonicityNot monotonic
2022-05-03T11:28:20.187160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
078
24.0%
329
 
8.9%
127
 
8.3%
220
 
6.2%
520
 
6.2%
814
 
4.3%
410
 
3.1%
79
 
2.8%
159
 
2.8%
168
 
2.5%
Other values (35)101
31.1%
ValueCountFrequency (%)
078
24.0%
127
 
8.3%
220
 
6.2%
329
 
8.9%
410
 
3.1%
520
 
6.2%
67
 
2.2%
79
 
2.8%
814
 
4.3%
92
 
0.6%
ValueCountFrequency (%)
581
 
0.3%
571
 
0.3%
541
 
0.3%
511
 
0.3%
491
 
0.3%
421
 
0.3%
411
 
0.3%
401
 
0.3%
393
0.9%
381
 
0.3%

A
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8923076923
Minimum0
Maximum10
Zeros190
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:20.281224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.516606398
Coefficient of variation (CV)1.699645101
Kurtosis8.036543472
Mean0.8923076923
Median Absolute Deviation (MAD)0
Skewness2.53917719
Sum290
Variance2.300094967
MonotonicityNot monotonic
2022-05-03T11:28:20.366680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0190
58.5%
170
 
21.5%
324
 
7.4%
223
 
7.1%
45
 
1.5%
55
 
1.5%
64
 
1.2%
82
 
0.6%
71
 
0.3%
101
 
0.3%
ValueCountFrequency (%)
0190
58.5%
170
 
21.5%
223
 
7.1%
324
 
7.4%
45
 
1.5%
55
 
1.5%
64
 
1.2%
71
 
0.3%
82
 
0.6%
101
 
0.3%
ValueCountFrequency (%)
101
 
0.3%
82
 
0.6%
71
 
0.3%
64
 
1.2%
55
 
1.5%
45
 
1.5%
324
 
7.4%
223
 
7.1%
170
 
21.5%
0190
58.5%

xA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct160
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8572307692
Minimum0
Maximum6.16
Zeros78
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:20.488765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.35
Q31.22
95-th percentile3.316
Maximum6.16
Range6.16
Interquartile range (IQR)1.19

Descriptive statistics

Standard deviation1.16594754
Coefficient of variation (CV)1.360132629
Kurtosis4.167960905
Mean0.8572307692
Median Absolute Deviation (MAD)0.35
Skewness1.980183777
Sum278.6
Variance1.359433666
MonotonicityNot monotonic
2022-05-03T11:28:20.584968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
078
 
24.0%
0.195
 
1.5%
0.035
 
1.5%
0.245
 
1.5%
0.125
 
1.5%
0.055
 
1.5%
0.274
 
1.2%
0.114
 
1.2%
0.064
 
1.2%
0.164
 
1.2%
Other values (150)206
63.4%
ValueCountFrequency (%)
078
24.0%
0.011
 
0.3%
0.021
 
0.3%
0.035
 
1.5%
0.042
 
0.6%
0.055
 
1.5%
0.064
 
1.2%
0.073
 
0.9%
0.083
 
0.9%
0.091
 
0.3%
ValueCountFrequency (%)
6.161
0.3%
5.521
0.3%
5.51
0.3%
5.41
0.3%
5.141
0.3%
51
0.3%
4.871
0.3%
4.751
0.3%
4.251
0.3%
41
0.3%

A-xA
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct150
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03510769231
Minimum-2.26
Maximum4.35
Zeros78
Zeros (%)24.0%
Negative162
Negative (%)49.8%
Memory size2.7 KiB
2022-05-03T11:28:20.707004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.26
5-th percentile-1.124
Q1-0.28
median0
Q30.12
95-th percentile1.67
Maximum4.35
Range6.61
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.8647859245
Coefficient of variation (CV)24.63237734
Kurtosis6.383272539
Mean0.03510769231
Median Absolute Deviation (MAD)0.24
Skewness1.74038987
Sum11.41
Variance0.7478546952
MonotonicityNot monotonic
2022-05-03T11:28:20.825166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
078
 
24.0%
-0.066
 
1.8%
-0.196
 
1.8%
-0.055
 
1.5%
-0.165
 
1.5%
-0.125
 
1.5%
-0.035
 
1.5%
-0.114
 
1.2%
-0.184
 
1.2%
-0.214
 
1.2%
Other values (140)203
62.5%
ValueCountFrequency (%)
-2.261
 
0.3%
-1.871
 
0.3%
-1.821
 
0.3%
-1.752
0.6%
-1.71
 
0.3%
-1.611
 
0.3%
-1.591
 
0.3%
-1.451
 
0.3%
-1.431
 
0.3%
-1.423
0.9%
ValueCountFrequency (%)
4.351
0.3%
3.951
0.3%
3.841
0.3%
3.751
0.3%
3.651
0.3%
3.361
0.3%
2.641
0.3%
2.421
0.3%
2.281
0.3%
2.191
0.3%

xG+xA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct206
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.181107692
Minimum0
Maximum16.79
Zeros48
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:20.947276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.17
median1.06
Q33.09
95-th percentile7.78
Maximum16.79
Range16.79
Interquartile range (IQR)2.92

Descriptive statistics

Standard deviation2.829103815
Coefficient of variation (CV)1.297094969
Kurtosis4.673392191
Mean2.181107692
Median Absolute Deviation (MAD)1.04
Skewness2.027102923
Sum708.86
Variance8.003828399
MonotonicityNot monotonic
2022-05-03T11:28:21.038192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048
 
14.8%
0.124
 
1.2%
0.174
 
1.2%
0.194
 
1.2%
0.054
 
1.2%
0.94
 
1.2%
0.464
 
1.2%
1.513
 
0.9%
1.733
 
0.9%
1.43
 
0.9%
Other values (196)244
75.1%
ValueCountFrequency (%)
048
14.8%
0.011
 
0.3%
0.023
 
0.9%
0.032
 
0.6%
0.042
 
0.6%
0.054
 
1.2%
0.062
 
0.6%
0.073
 
0.9%
0.092
 
0.6%
0.12
 
0.6%
ValueCountFrequency (%)
16.791
0.3%
13.831
0.3%
13.041
0.3%
131
0.3%
12.471
0.3%
12.091
0.3%
11.041
0.3%
10.741
0.3%
9.741
0.3%
9.671
0.3%

PA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct110
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7093538462
Minimum0
Maximum9.52
Zeros192
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:21.154464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.92
95-th percentile3.634
Maximum9.52
Range9.52
Interquartile range (IQR)0.92

Descriptive statistics

Standard deviation1.345001451
Coefficient of variation (CV)1.89609383
Kurtosis10.35539995
Mean0.7093538462
Median Absolute Deviation (MAD)0
Skewness2.905125817
Sum230.54
Variance1.809028902
MonotonicityNot monotonic
2022-05-03T11:28:21.266736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0192
59.1%
0.664
 
1.2%
1.013
 
0.9%
0.213
 
0.9%
0.712
 
0.6%
1.182
 
0.6%
0.952
 
0.6%
0.742
 
0.6%
0.732
 
0.6%
4.542
 
0.6%
Other values (100)111
34.2%
ValueCountFrequency (%)
0192
59.1%
0.031
 
0.3%
0.061
 
0.3%
0.091
 
0.3%
0.11
 
0.3%
0.141
 
0.3%
0.213
 
0.9%
0.271
 
0.3%
0.361
 
0.3%
0.392
 
0.6%
ValueCountFrequency (%)
9.521
0.3%
7.171
0.3%
6.931
0.3%
5.951
0.3%
5.481
0.3%
5.412
0.6%
5.141
0.3%
4.861
0.3%
4.691
0.3%
4.571
0.3%

xPA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct151
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8895076923
Minimum0
Maximum9.82
Zeros69
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:21.381362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.35
Q31.09
95-th percentile3.94
Maximum9.82
Range9.82
Interquartile range (IQR)1.06

Descriptive statistics

Standard deviation1.440135583
Coefficient of variation (CV)1.61902544
Kurtosis11.12351446
Mean0.8895076923
Median Absolute Deviation (MAD)0.35
Skewness3.010178586
Sum289.09
Variance2.073990498
MonotonicityNot monotonic
2022-05-03T11:28:21.486591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069
 
21.2%
0.027
 
2.2%
0.056
 
1.8%
0.356
 
1.8%
0.096
 
1.8%
0.385
 
1.5%
0.075
 
1.5%
0.215
 
1.5%
0.014
 
1.2%
0.154
 
1.2%
Other values (141)208
64.0%
ValueCountFrequency (%)
069
21.2%
0.014
 
1.2%
0.027
 
2.2%
0.033
 
0.9%
0.044
 
1.2%
0.056
 
1.8%
0.063
 
0.9%
0.075
 
1.5%
0.082
 
0.6%
0.096
 
1.8%
ValueCountFrequency (%)
9.821
0.3%
8.561
0.3%
8.451
0.3%
6.671
0.3%
6.51
0.3%
6.441
0.3%
6.071
0.3%
5.231
0.3%
5.111
0.3%
5.041
0.3%

Passes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct275
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean471.6830769
Minimum0
Maximum2016
Zeros5
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:21.607706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2
Q1117
median364
Q3723
95-th percentile1320.8
Maximum2016
Range2016
Interquartile range (IQR)606

Descriptive statistics

Standard deviation423.5205053
Coefficient of variation (CV)0.8978920932
Kurtosis0.148843561
Mean471.6830769
Median Absolute Deviation (MAD)281
Skewness0.9272436653
Sum153297
Variance179369.6184
MonotonicityNot monotonic
2022-05-03T11:28:21.707818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
1.5%
15
 
1.5%
1023
 
0.9%
2893
 
0.9%
283
 
0.9%
273
 
0.9%
1133
 
0.9%
6193
 
0.9%
53
 
0.9%
1813
 
0.9%
Other values (265)291
89.5%
ValueCountFrequency (%)
05
1.5%
15
1.5%
21
 
0.3%
32
 
0.6%
41
 
0.3%
53
0.9%
63
0.9%
71
 
0.3%
82
 
0.6%
101
 
0.3%
ValueCountFrequency (%)
20161
0.3%
16451
0.3%
15741
0.3%
15661
0.3%
15611
0.3%
15471
0.3%
15041
0.3%
14881
0.3%
14692
0.6%
14441
0.3%

Pass %
Categorical

HIGH CARDINALITY

Distinct197
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0%
 
8
83.8%
 
6
66.7%
 
6
76.9%
 
5
78.5%
 
5
Other values (192)
295 

Length

Max length6
Median length5
Mean length4.981538462
Min length4

Characters and Unicode

Total characters1619
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)37.5%

Sample

1st row100.0%
2nd row82.4%
3rd row82.1%
4th row73.8%
5th row88.5%

Common Values

ValueCountFrequency (%)
0.0%8
 
2.5%
83.8%6
 
1.8%
66.7%6
 
1.8%
76.9%5
 
1.5%
78.5%5
 
1.5%
80.0%5
 
1.5%
81.1%4
 
1.2%
88.8%4
 
1.2%
80.1%4
 
1.2%
80.5%4
 
1.2%
Other values (187)274
84.3%

Length

2022-05-03T11:28:21.837426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.08
 
2.5%
66.76
 
1.8%
83.86
 
1.8%
76.95
 
1.5%
78.55
 
1.5%
80.05
 
1.5%
80.54
 
1.2%
87.04
 
1.2%
78.64
 
1.2%
80.14
 
1.2%
Other values (187)274
84.3%

Most occurring characters

ValueCountFrequency (%)
.325
20.1%
%325
20.1%
8196
12.1%
7177
10.9%
0118
 
7.3%
6111
 
6.9%
371
 
4.4%
571
 
4.4%
165
 
4.0%
957
 
3.5%
Other values (2)103
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number969
59.9%
Other Punctuation650
40.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8196
20.2%
7177
18.3%
0118
12.2%
6111
11.5%
371
 
7.3%
571
 
7.3%
165
 
6.7%
957
 
5.9%
252
 
5.4%
451
 
5.3%
Other Punctuation
ValueCountFrequency (%)
.325
50.0%
%325
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1619
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.325
20.1%
%325
20.1%
8196
12.1%
7177
10.9%
0118
 
7.3%
6111
 
6.9%
371
 
4.4%
571
 
4.4%
165
 
4.0%
957
 
3.5%
Other values (2)103
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.325
20.1%
%325
20.1%
8196
12.1%
7177
10.9%
0118
 
7.3%
6111
 
6.9%
371
 
4.4%
571
 
4.4%
165
 
4.0%
957
 
3.5%
Other values (2)103
 
6.4%

xPass %
Categorical

HIGH CARDINALITY
UNIFORM

Distinct181
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
81.2%
 
5
75.2%
 
5
81.8%
 
5
0.0%
 
5
77.8%
 
5
Other values (176)
300 

Length

Max length5
Median length5
Mean length4.984615385
Min length4

Characters and Unicode

Total characters1620
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)30.8%

Sample

1st row95.0%
2nd row80.3%
3rd row81.0%
4th row71.3%
5th row87.3%

Common Values

ValueCountFrequency (%)
81.2%5
 
1.5%
75.2%5
 
1.5%
81.8%5
 
1.5%
0.0%5
 
1.5%
77.8%5
 
1.5%
80.7%4
 
1.2%
82.1%4
 
1.2%
79.1%4
 
1.2%
73.8%4
 
1.2%
72.8%4
 
1.2%
Other values (171)280
86.2%

Length

2022-05-03T11:28:21.936546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
81.25
 
1.5%
81.85
 
1.5%
0.05
 
1.5%
77.85
 
1.5%
75.25
 
1.5%
66.84
 
1.2%
79.34
 
1.2%
76.34
 
1.2%
72.74
 
1.2%
76.74
 
1.2%
Other values (171)280
86.2%

Most occurring characters

ValueCountFrequency (%)
.325
20.1%
%325
20.1%
7213
13.1%
8202
12.5%
6109
 
6.7%
083
 
5.1%
269
 
4.3%
569
 
4.3%
166
 
4.1%
364
 
4.0%
Other values (2)95
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number970
59.9%
Other Punctuation650
40.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7213
22.0%
8202
20.8%
6109
11.2%
083
 
8.6%
269
 
7.1%
569
 
7.1%
166
 
6.8%
364
 
6.6%
952
 
5.4%
443
 
4.4%
Other Punctuation
ValueCountFrequency (%)
.325
50.0%
%325
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.325
20.1%
%325
20.1%
7213
13.1%
8202
12.5%
6109
 
6.7%
083
 
5.1%
269
 
4.3%
569
 
4.3%
166
 
4.1%
364
 
4.0%
Other values (2)95
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.325
20.1%
%325
20.1%
7213
13.1%
8202
12.5%
6109
 
6.7%
083
 
5.1%
269
 
4.3%
569
 
4.3%
166
 
4.1%
364
 
4.0%
Other values (2)95
 
5.9%

Score
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct310
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.296369231
Minimum-50.96
Maximum47.12
Zeros5
Zeros (%)1.5%
Negative161
Negative (%)49.5%
Memory size2.7 KiB
2022-05-03T11:28:22.036648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-50.96
5-th percentile-16.416
Q1-4.89
median0
Q36.16
95-th percentile21.636
Maximum47.12
Range98.08
Interquartile range (IQR)11.05

Descriptive statistics

Standard deviation12.06280401
Coefficient of variation (CV)9.305068124
Kurtosis2.756806137
Mean1.296369231
Median Absolute Deviation (MAD)5.18
Skewness0.7426989611
Sum421.32
Variance145.5112405
MonotonicityNot monotonic
2022-05-03T11:28:22.159343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
1.5%
0.072
 
0.6%
-1.752
 
0.6%
-1.212
 
0.6%
0.632
 
0.6%
0.932
 
0.6%
-1.972
 
0.6%
0.962
 
0.6%
8.372
 
0.6%
-4.722
 
0.6%
Other values (300)302
92.9%
ValueCountFrequency (%)
-50.961
0.3%
-29.081
0.3%
-22.051
0.3%
-20.21
0.3%
-19.981
0.3%
-19.711
0.3%
-19.631
0.3%
-19.611
0.3%
-19.11
0.3%
-18.581
0.3%
ValueCountFrequency (%)
47.121
0.3%
43.521
0.3%
41.371
0.3%
39.861
0.3%
37.121
0.3%
37.081
0.3%
36.581
0.3%
35.691
0.3%
34.481
0.3%
33.231
0.3%

Per100
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct286
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.021384615
Minimum-46.74
Maximum19.91
Zeros5
Zeros (%)1.5%
Negative161
Negative (%)49.5%
Memory size2.7 KiB
2022-05-03T11:28:22.294030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-46.74
5-th percentile-7.538
Q1-2.46
median0
Q31.68
95-th percentile4.174
Maximum19.91
Range66.65
Interquartile range (IQR)4.14

Descriptive statistics

Standard deviation6.035789473
Coefficient of variation (CV)-5.909418824
Kurtosis21.65185655
Mean-1.021384615
Median Absolute Deviation (MAD)1.94
Skewness-3.281929559
Sum-331.95
Variance36.43075456
MonotonicityNot monotonic
2022-05-03T11:28:22.403130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
1.5%
2.33
 
0.9%
-0.393
 
0.9%
1.273
 
0.9%
-0.342
 
0.6%
0.292
 
0.6%
2.512
 
0.6%
2.162
 
0.6%
-5.262
 
0.6%
-4.062
 
0.6%
Other values (276)299
92.0%
ValueCountFrequency (%)
-46.741
0.3%
-40.781
0.3%
-40.471
0.3%
-21.281
0.3%
-18.61
0.3%
-17.971
0.3%
-16.861
0.3%
-15.731
0.3%
-15.211
0.3%
-15.21
0.3%
ValueCountFrequency (%)
19.911
0.3%
18.381
0.3%
13.531
0.3%
12.741
0.3%
9.471
0.3%
9.041
0.3%
7.991
0.3%
7.551
0.3%
6.461
0.3%
5.971
0.3%

Distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct283
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.7964
Minimum0
Maximum51.02
Zeros8
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:22.535971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.696
Q117.67
median20.56
Q323.28
95-th percentile31.614
Maximum51.02
Range51.02
Interquartile range (IQR)5.61

Descriptive statistics

Standard deviation6.313109398
Coefficient of variation (CV)0.3035674154
Kurtosis4.545086896
Mean20.7964
Median Absolute Deviation (MAD)2.85
Skewness0.2428918601
Sum6758.83
Variance39.85535027
MonotonicityNot monotonic
2022-05-03T11:28:22.644753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
2.5%
17.453
 
0.9%
24.233
 
0.9%
22.333
 
0.9%
21.683
 
0.9%
27.222
 
0.6%
20.652
 
0.6%
18.152
 
0.6%
17.812
 
0.6%
22.912
 
0.6%
Other values (273)295
90.8%
ValueCountFrequency (%)
08
2.5%
6.971
 
0.3%
8.051
 
0.3%
10.51
 
0.3%
11.781
 
0.3%
12.111
 
0.3%
131
 
0.3%
13.171
 
0.3%
13.671
 
0.3%
13.681
 
0.3%
ValueCountFrequency (%)
51.021
0.3%
46.131
0.3%
41.881
0.3%
40.731
0.3%
39.071
0.3%
36.771
0.3%
35.671
0.3%
34.81
0.3%
34.441
0.3%
34.361
0.3%

Vertical
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct293
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.541661538
Minimum-17.77
Maximum45.08
Zeros8
Zeros (%)2.5%
Negative53
Negative (%)16.3%
Memory size2.7 KiB
2022-05-03T11:28:22.740925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-17.77
5-th percentile-2.528
Q10.92
median3.98
Q38.22
95-th percentile21.77
Maximum45.08
Range62.85
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation7.672053322
Coefficient of variation (CV)1.384431956
Kurtosis4.436260466
Mean5.541661538
Median Absolute Deviation (MAD)3.55
Skewness1.659907128
Sum1801.04
Variance58.86040217
MonotonicityNot monotonic
2022-05-03T11:28:22.828986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
2.5%
-1.033
 
0.9%
4.112
 
0.6%
1.062
 
0.6%
-0.642
 
0.6%
1.732
 
0.6%
9.42
 
0.6%
3.982
 
0.6%
2.852
 
0.6%
9.452
 
0.6%
Other values (283)298
91.7%
ValueCountFrequency (%)
-17.771
0.3%
-10.351
0.3%
-8.831
0.3%
-8.681
0.3%
-5.981
0.3%
-5.871
0.3%
-4.881
0.3%
-4.131
0.3%
-3.771
0.3%
-3.581
0.3%
ValueCountFrequency (%)
45.081
0.3%
37.391
0.3%
33.461
0.3%
33.381
0.3%
30.21
0.3%
28.371
0.3%
27.671
0.3%
27.091
0.3%
26.641
0.3%
25.781
0.3%

Touch %
Categorical

HIGH CARDINALITY

Distinct116
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
1.9%
 
7
5.1%
 
6
3.6%
 
6
6.7%
 
6
7.1%
 
6
Other values (111)
294 

Length

Max length5
Median length4
Mean length4.126153846
Min length4

Characters and Unicode

Total characters1341
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)6.5%

Sample

1st row0.2%
2nd row13.6%
3rd row9.6%
4th row10.3%
5th row7.2%

Common Values

ValueCountFrequency (%)
1.9%7
 
2.2%
5.1%6
 
1.8%
3.6%6
 
1.8%
6.7%6
 
1.8%
7.1%6
 
1.8%
5.9%6
 
1.8%
4.4%6
 
1.8%
8.0%5
 
1.5%
4.8%5
 
1.5%
7.2%5
 
1.5%
Other values (106)267
82.2%

Length

2022-05-03T11:28:22.946375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1.97
 
2.2%
3.66
 
1.8%
6.76
 
1.8%
7.16
 
1.8%
5.96
 
1.8%
4.46
 
1.8%
5.16
 
1.8%
5.45
 
1.5%
0.45
 
1.5%
4.35
 
1.5%
Other values (106)267
82.2%

Most occurring characters

ValueCountFrequency (%)
.325
24.2%
%325
24.2%
1111
 
8.3%
473
 
5.4%
072
 
5.4%
567
 
5.0%
765
 
4.8%
663
 
4.7%
862
 
4.6%
261
 
4.5%
Other values (2)117
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number691
51.5%
Other Punctuation650
48.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1111
16.1%
473
10.6%
072
10.4%
567
9.7%
765
9.4%
663
9.1%
862
9.0%
261
8.8%
959
8.5%
358
8.4%
Other Punctuation
ValueCountFrequency (%)
.325
50.0%
%325
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1341
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.325
24.2%
%325
24.2%
1111
 
8.3%
473
 
5.4%
072
 
5.4%
567
 
5.0%
765
 
4.8%
663
 
4.7%
862
 
4.6%
261
 
4.5%
Other values (2)117
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.325
24.2%
%325
24.2%
1111
 
8.3%
473
 
5.4%
072
 
5.4%
567
 
5.0%
765
 
4.8%
663
 
4.7%
862
 
4.6%
261
 
4.5%
Other values (2)117
 
8.7%

Games
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.28307692
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2022-05-03T11:28:23.045424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median16
Q323
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.040368472
Coefficient of variation (CV)0.5915280357
Kurtosis-1.351123134
Mean15.28307692
Median Absolute Deviation (MAD)8
Skewness-0.2146601894
Sum4967
Variance81.72826211
MonotonicityNot monotonic
2022-05-03T11:28:23.143287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2730
 
9.2%
125
 
7.7%
2119
 
5.8%
217
 
5.2%
2516
 
4.9%
2315
 
4.6%
1514
 
4.3%
2813
 
4.0%
313
 
4.0%
2412
 
3.7%
Other values (18)151
46.5%
ValueCountFrequency (%)
125
7.7%
217
5.2%
313
4.0%
411
3.4%
55
 
1.5%
65
 
1.5%
78
 
2.5%
89
 
2.8%
910
 
3.1%
108
 
2.5%
ValueCountFrequency (%)
2813
4.0%
2730
9.2%
269
 
2.8%
2516
4.9%
2412
 
3.7%
2315
4.6%
229
 
2.8%
2119
5.8%
2011
 
3.4%
1911
 
3.4%

Dribbling
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct105
Distinct (%)35.4%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean-0.01616161616
Minimum-1.57
Maximum2.85
Zeros17
Zeros (%)5.2%
Negative173
Negative (%)53.2%
Memory size2.7 KiB
2022-05-03T11:28:23.238713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.57
5-th percentile-0.47
Q1-0.16
median-0.02
Q30.06
95-th percentile0.546
Maximum2.85
Range4.42
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.3730898388
Coefficient of variation (CV)-23.08493378
Kurtosis14.29794275
Mean-0.01616161616
Median Absolute Deviation (MAD)0.12
Skewness1.793799916
Sum-4.8
Variance0.1391960278
MonotonicityNot monotonic
2022-05-03T11:28:23.335709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-017
 
5.2%
-0.0214
 
4.3%
-0.0112
 
3.7%
-0.0312
 
3.7%
0.0111
 
3.4%
-0.1210
 
3.1%
-0.157
 
2.2%
0.057
 
2.2%
-0.187
 
2.2%
0.066
 
1.8%
Other values (95)194
59.7%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-1.571
0.3%
-1.171
0.3%
-1.151
0.3%
-11
0.3%
-0.961
0.3%
-0.861
0.3%
-0.691
0.3%
-0.641
0.3%
-0.631
0.3%
-0.621
0.3%
ValueCountFrequency (%)
2.851
0.3%
1.371
0.3%
1.321
0.3%
1.271
0.3%
1.081
0.3%
1.061
0.3%
0.942
0.6%
0.881
0.3%
0.821
0.3%
0.751
0.3%

Fouling
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct67
Distinct (%)22.6%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean-0.002255892256
Minimum-0.54
Maximum0.96
Zeros45
Zeros (%)13.8%
Negative129
Negative (%)39.7%
Memory size2.7 KiB
2022-05-03T11:28:23.454709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.54
5-th percentile-0.264
Q1-0.06
median0
Q30.05
95-th percentile0.24
Maximum0.96
Range1.5
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.154270171
Coefficient of variation (CV)-68.38543399
Kurtosis5.948828403
Mean-0.002255892256
Median Absolute Deviation (MAD)0.06
Skewness0.6669126541
Sum-0.67
Variance0.02379928565
MonotonicityNot monotonic
2022-05-03T11:28:23.567891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045
 
13.8%
-0.0117
 
5.2%
0.0116
 
4.9%
-0.0214
 
4.3%
0.0212
 
3.7%
0.0411
 
3.4%
-0.069
 
2.8%
0.038
 
2.5%
0.127
 
2.2%
-0.077
 
2.2%
Other values (57)151
46.5%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-0.541
0.3%
-0.422
0.6%
-0.392
0.6%
-0.381
0.3%
-0.352
0.6%
-0.341
0.3%
-0.322
0.6%
-0.311
0.3%
-0.291
0.3%
-0.282
0.6%
ValueCountFrequency (%)
0.961
 
0.3%
0.481
 
0.3%
0.451
 
0.3%
0.441
 
0.3%
0.421
 
0.3%
0.331
 
0.3%
0.311
 
0.3%
0.31
 
0.3%
0.293
0.9%
0.252
0.6%

Interrupting
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct105
Distinct (%)35.4%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean0.006666666667
Minimum-1.15
Maximum2.02
Zeros18
Zeros (%)5.5%
Negative149
Negative (%)45.8%
Memory size2.7 KiB
2022-05-03T11:28:23.686260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.15
5-th percentile-0.426
Q1-0.12
median-0.01
Q30.12
95-th percentile0.51
Maximum2.02
Range3.17
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.3089542081
Coefficient of variation (CV)46.34313122
Kurtosis6.991265339
Mean0.006666666667
Median Absolute Deviation (MAD)0.12
Skewness0.8258864576
Sum1.98
Variance0.0954527027
MonotonicityNot monotonic
2022-05-03T11:28:23.788311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-018
 
5.5%
-0.0113
 
4.0%
-0.0212
 
3.7%
-0.0311
 
3.4%
0.0111
 
3.4%
0.0210
 
3.1%
-0.089
 
2.8%
0.068
 
2.5%
-0.077
 
2.2%
-0.046
 
1.8%
Other values (95)192
59.1%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-1.151
0.3%
-1.021
0.3%
-0.881
0.3%
-0.742
0.6%
-0.721
0.3%
-0.671
0.3%
-0.641
0.3%
-0.621
0.3%
-0.591
0.3%
-0.531
0.3%
ValueCountFrequency (%)
2.021
0.3%
0.881
0.3%
0.831
0.3%
0.81
0.3%
0.752
0.6%
0.721
0.3%
0.682
0.6%
0.661
0.3%
0.651
0.3%
0.641
0.3%

Passing
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct118
Distinct (%)39.7%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean0.04168350168
Minimum-1.4
Maximum2.13
Zeros19
Zeros (%)5.8%
Negative143
Negative (%)44.0%
Memory size2.7 KiB
2022-05-03T11:28:23.898631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.4
5-th percentile-0.482
Q1-0.13
median0
Q30.19
95-th percentile0.702
Maximum2.13
Range3.53
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.3782648786
Coefficient of variation (CV)9.074690546
Kurtosis4.727710974
Mean0.04168350168
Median Absolute Deviation (MAD)0.17
Skewness1.045949886
Sum12.38
Variance0.1430843184
MonotonicityNot monotonic
2022-05-03T11:28:24.008901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-019
 
5.8%
-0.0111
 
3.4%
-0.038
 
2.5%
-0.057
 
2.2%
0.086
 
1.8%
-0.026
 
1.8%
-0.096
 
1.8%
-0.066
 
1.8%
0.026
 
1.8%
0.016
 
1.8%
Other values (108)216
66.5%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-1.41
0.3%
-0.862
0.6%
-0.821
0.3%
-0.771
0.3%
-0.681
0.3%
-0.631
0.3%
-0.621
0.3%
-0.612
0.6%
-0.61
0.3%
-0.561
0.3%
ValueCountFrequency (%)
2.131
0.3%
1.41
0.3%
1.321
0.3%
1.241
0.3%
1.121
0.3%
1.071
0.3%
1.042
0.6%
0.981
0.3%
0.941
0.3%
0.911
0.3%

Receiving
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct119
Distinct (%)40.1%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean0.00734006734
Minimum-1.03
Maximum2.63
Zeros12
Zeros (%)3.7%
Negative177
Negative (%)54.5%
Memory size2.7 KiB
2022-05-03T11:28:24.147059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.03
5-th percentile-0.48
Q1-0.23
median-0.04
Q30.11
95-th percentile0.798
Maximum2.63
Range3.66
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.4160699276
Coefficient of variation (CV)56.68475619
Kurtosis8.058350678
Mean0.00734006734
Median Absolute Deviation (MAD)0.18
Skewness1.971102315
Sum2.18
Variance0.1731141846
MonotonicityNot monotonic
2022-05-03T11:28:24.255888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0113
 
4.0%
-012
 
3.7%
-0.038
 
2.5%
-0.048
 
2.5%
-0.117
 
2.2%
-0.257
 
2.2%
-0.027
 
2.2%
-0.176
 
1.8%
-0.276
 
1.8%
0.066
 
1.8%
Other values (109)217
66.8%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-1.031
0.3%
-11
0.3%
-0.911
0.3%
-0.741
0.3%
-0.711
0.3%
-0.652
0.6%
-0.631
0.3%
-0.621
0.3%
-0.581
0.3%
-0.521
0.3%
ValueCountFrequency (%)
2.631
 
0.3%
1.981
 
0.3%
1.621
 
0.3%
1.51
 
0.3%
1.381
 
0.3%
1.351
 
0.3%
1.071
 
0.3%
1.051
 
0.3%
0.973
0.9%
0.951
 
0.3%

Shooting
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct74
Distinct (%)24.9%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean-0.001717171717
Minimum-0.65
Maximum1.06
Zeros19
Zeros (%)5.8%
Negative173
Negative (%)53.2%
Memory size2.7 KiB
2022-05-03T11:28:24.377425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.65
5-th percentile-0.25
Q1-0.1
median-0.02
Q30.04
95-th percentile0.322
Maximum1.06
Range1.71
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1905772192
Coefficient of variation (CV)-110.9832041
Kurtosis6.364752815
Mean-0.001717171717
Median Absolute Deviation (MAD)0.07
Skewness1.66936657
Sum-0.51
Variance0.03631967649
MonotonicityNot monotonic
2022-05-03T11:28:24.487628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-019
 
5.8%
-0.0118
 
5.5%
-0.0616
 
4.9%
-0.0214
 
4.3%
-0.1112
 
3.7%
-0.0412
 
3.7%
0.0111
 
3.4%
-0.099
 
2.8%
-0.129
 
2.8%
-0.039
 
2.8%
Other values (64)168
51.7%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-0.651
 
0.3%
-0.451
 
0.3%
-0.42
0.6%
-0.322
0.6%
-0.311
 
0.3%
-0.31
 
0.3%
-0.281
 
0.3%
-0.264
1.2%
-0.253
0.9%
-0.242
0.6%
ValueCountFrequency (%)
1.061
0.3%
0.811
0.3%
0.771
0.3%
0.721
0.3%
0.651
0.3%
0.621
0.3%
0.591
0.3%
0.521
0.3%
0.51
0.3%
0.481
0.3%

Goals Added
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct171
Distinct (%)57.6%
Missing28
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean0.03505050505
Minimum-1.9
Maximum3.84
Zeros5
Zeros (%)1.5%
Negative159
Negative (%)48.9%
Memory size2.7 KiB
2022-05-03T11:28:24.606842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.9
5-th percentile-1.092
Q1-0.35
median-0.02
Q30.27
95-th percentile1.456
Maximum3.84
Range5.74
Interquartile range (IQR)0.62

Descriptive statistics

Standard deviation0.7613990152
Coefficient of variation (CV)21.72291138
Kurtosis3.145249023
Mean0.03505050505
Median Absolute Deviation (MAD)0.33
Skewness1.105422458
Sum10.41
Variance0.5797284603
MonotonicityNot monotonic
2022-05-03T11:28:24.715985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.028
 
2.5%
-0.526
 
1.8%
-0.016
 
1.8%
0.096
 
1.8%
0.036
 
1.8%
-0.095
 
1.5%
05
 
1.5%
0.015
 
1.5%
-0.634
 
1.2%
-0.124
 
1.2%
Other values (161)242
74.5%
(Missing)28
 
8.6%
ValueCountFrequency (%)
-1.91
0.3%
-1.541
0.3%
-1.491
0.3%
-1.451
0.3%
-1.421
0.3%
-1.411
0.3%
-1.41
0.3%
-1.381
0.3%
-1.321
0.3%
-1.261
0.3%
ValueCountFrequency (%)
3.841
0.3%
2.831
0.3%
2.651
0.3%
2.491
0.3%
2.281
0.3%
2.011
0.3%
21
0.3%
1.981
0.3%
1.951
0.3%
1.791
0.3%

Interactions

2022-05-03T11:28:11.976446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:26:51.890248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:26:54.822906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:26:57.707535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:01.254754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:04.459575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:07.495728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:10.528361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:13.573418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:16.649409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:19.628071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:23.047239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:26.066313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:29.161528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:31.931859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:34.828002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:38.486121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:41.189969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:44.375116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:47.259063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:50.281942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:53.171792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:56.555047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:59.854743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:28:02.845708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:28:06.000920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:28:09.035391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:28:12.086823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:26:51.988048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:26:54.934975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:26:57.820741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:01.363204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:04.572931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-03T11:27:07.599382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2022-05-03T11:28:24.846835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-03T11:28:25.164521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-03T11:28:25.499925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-03T11:28:25.791642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-03T11:28:25.932660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-03T11:28:15.003863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-03T11:28:16.494479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-03T11:28:16.757186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-03T11:28:17.018600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PlayerTeamSeasonPositionMinutesShotsSoTGxGxPlaceG-xGKeyPAxAA-xAxG+xAPAxPAPassesPass %xPass %ScorePer100DistanceVerticalTouch %GamesDribblingFoulingInterruptingPassingReceivingShootingGoals Added
0Aaron MesserNC2021CB10000.000.000.00000.000.000.000.000.001100.0%95.0%0.055.0410.50-10.350.2%1-0.000.000.01-0.00-0.00-0.000.01
1Aaron MolloyMAD2021DM2626441442.310.511.695433.21-0.215.531.961.50201682.4%80.3%43.522.1622.845.5113.6%270.720.120.681.24-0.260.162.65
2Aaron WalkerGVL2021CM235632852.551.562.451731.211.793.761.771.68101182.1%81.0%10.731.0622.724.979.6%25-0.39-0.040.20-0.10-0.320.37-0.28
3Abdi MohamedGVL2021FB212911300.57-0.22-0.572533.19-0.193.760.000.35117373.8%71.3%29.432.5122.585.8910.3%24-0.18-0.170.190.56-0.25-0.140.00
4Abdul Illal OsumanuOMA2021CB12681000.07-0.07-0.07000.000.000.070.000.0955088.5%87.3%6.881.2522.736.257.2%15-0.110.090.64-0.24-0.35-0.18-0.16
5Abel CaputoFTL2021DM12347110.77-0.010.23800.66-0.661.430.390.4246484.9%84.7%1.160.2520.772.014.9%21-0.040.24-0.12-0.22-0.11-0.22-0.47
6Abuchi ObinwaTRM2021CM16549000.43-0.43-0.43810.610.391.050.000.2370685.0%86.3%-9.53-1.3520.651.596.9%23-0.28-0.18-0.29-0.28-0.62-0.24-1.90
7Adrian BillhardtTRM2021W2259200.63-0.40-0.63210.200.800.830.000.447364.4%69.4%-3.69-5.0521.010.935.2%40.02-0.020.07-0.000.160.100.32
8Aimé MabikaFTL2021CB13885200.82-0.47-0.82100.29-0.291.100.000.2382290.4%88.2%17.682.1523.497.4511.6%140.94-0.062.020.05-0.06-0.062.83
9Akira FitzgeraldRIC2021GK26850000.000.000.00100.09-0.090.090.000.0084277.8%77.4%3.510.4233.6623.797.0%27NaNNaNNaNNaNNaNNaNNaN

Last rows

PlayerTeamSeasonPositionMinutesShotsSoTGxGxPlaceG-xGKeyPAxAA-xAxG+xAPAxPAPassesPass %xPass %ScorePer100DistanceVerticalTouch %GamesDribblingFoulingInterruptingPassingReceivingShootingGoals Added
315UalefiCHA2021DM8742200.080.19-0.08300.10-0.100.190.000.0237088.1%86.1%7.291.9721.943.444.7%16-0.050.050.10-0.03-0.22-0.13-0.29
316Venton EvansFTL2021W1569351542.970.371.03821.350.654.331.191.4041077.6%79.1%-6.29-1.5317.70-1.755.4%21-0.63-0.420.19-0.400.040.01-1.22
317Victor FalckRIC2021CM168913400.71-0.28-0.71810.270.730.980.000.6466276.9%77.6%-4.79-0.7222.966.967.4%220.070.040.030.08-0.25-0.26-0.28
318Vincenzo CandelaTRM2021CM91213500.56-0.20-0.56510.440.561.000.000.4737686.2%84.8%5.121.3620.452.854.2%20-0.100.000.51-0.15-0.32-0.10-0.17
319Wallis LapsleyTUC2021GK26720000.000.000.00000.000.000.000.000.0073576.2%74.5%12.731.7331.9623.115.4%27NaNNaNNaNNaNNaNNaNNaN
320William MejiaGVL2021W440000.000.000.00000.000.000.000.000.001172.7%69.9%0.312.8323.5115.230.8%4-0.02-0.00-0.030.00-0.03-0.02-0.09
321Yannik OettlNER2021GK1950000.000.000.00000.000.000.000.000.005084.0%79.6%2.214.4229.0721.853.2%3NaNNaNNaNNaNNaNNaNNaN
322Yekeson SubahNC2021ST251000.43-0.43-0.43000.000.000.430.000.74650.0%48.8%0.071.2312.11-8.680.9%2-0.020.01-0.01-0.010.240.000.21
323Yoskar GalvanOMA2021W460000.000.000.00000.000.000.000.000.001464.3%72.8%-1.19-8.5015.781.731.8%2-0.020.00-0.00-0.01-0.04-0.03-0.09
324Zacarías Morán CorreaRIC2021DM243812210.300.220.703833.32-0.323.621.540.25108677.5%76.3%13.301.2224.649.4310.5%250.10-0.13-0.020.91-0.63-0.28-0.05